Intelligent Diagnosis and Prognosis of Industrial Networked Systems: 1st Edition (Paperback) book cover

Intelligent Diagnosis and Prognosis of Industrial Networked Systems

1st Edition

By Chee Khiang Pang, Frank L. Lewis, Tong Heng Lee, Zhao Yang Dong

CRC Press

332 pages | 66 B/W Illus.

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In an era of intense competition where plant operating efficiencies must be maximized, downtime due to machinery failure has become more costly. To cut operating costs and increase revenues, industries have an urgent need to predict fault progression and remaining lifespan of industrial machines, processes, and systems. An engineer who mounts an acoustic sensor onto a spindle motor wants to know when the ball bearings will wear out without having to halt the ongoing milling processes. A scientist working on sensor networks wants to know which sensors are redundant and can be pruned off to save operational and computational overheads. These scenarios illustrate a need for new and unified perspectives in system analysis and design for engineering applications.

Intelligent Diagnosis and Prognosis of Industrial Networked Systems proposes linear mathematical tool sets that can be applied to realistic engineering systems. The book offers an overview of the fundamentals of vectors, matrices, and linear systems theory required for intelligent diagnosis and prognosis of industrial networked systems. Building on this theory, it then develops automated mathematical machineries and formal decision software tools for real-world applications.

The book includes portable tool sets for many industrial applications, including:

  • Forecasting machine tool wear in industrial cutting machines
  • Reduction of sensors and features for industrial fault detection and isolation (FDI)
  • Identification of critical resonant modes in mechatronic systems for system design of R&D
  • Probabilistic small-signal stability in large-scale interconnected power systems
  • Discrete event command and control for military applications

The book also proposes future directions for intelligent diagnosis and prognosis in energy-efficient manufacturing, life cycle assessment, and systems of systems architecture. Written in a concise and accessible style, it presents tools that are mathematically rigorous but not involved. Bridging academia, research, and industry, this reference supplies the know-how for engineers and managers making decisions about equipment maintenance, as well as researchers and students in the field.

Table of Contents


Diagnosis and Prognosis



Applications in Industrial Networked Systems

Modal Parametric Identification (MPI)

Dominant Feature Identification (DFI)

Probabilistic Small Signal Stability Assessment

Discrete Event Command and Control

Vectors, Matrices, and Linear Systems

Fundamental Concepts



Linear Systems

Introduction to Linear Systems

State-Space Representation of LTI Systems

Linearization of Non-Linear Systems

Eigenvalue Decomposition and Sensitivity

Eigenvalue and Eigenvector

Eigenvalue Decomposition

Generalized Eigenvectors

Eigenvalue Sensitivity to Non-Deterministic System Parameters

Eigenvalue Sensitivity to Link Parameters

Singular Value Decomposition (SVD) and Applications

Singular Value Decomposition (SVD)

Norms, Rank, and Condition Number


Least Squares Solution

Minimum-Norm Solution Using SVD

Boolean Matrices

Binary Relation


Discrete-Event Systems


Modal Parametric Identification (MPI)


Servo-Mechanical-Prototype Production Cycle

Modal Summation

Pole-Zero Product

Lumped Polynomial

Systems Design Approach

Modal Parametric Identification (MPI) Algorithm

Natural Frequencies fi and Damping Ratios ζi

Reformulation Using Forsythe’s Orthogonal Polynomials

Residues Ri

Error Analysis

Industrial Application: Hard Disk Drive Servo Systems

Results and Discussions


Dominant Feature Identification (DFI)


Principal Component Analysis (PCA)

Approximation of Linear Transformation X

Approximation in Range Space by Principal Components

Dominant Feature Identification (DFI)

Data Compression

Selection of Dominant Features

Error Analysis

Simplified Computations

Time Series Forecasting Using Force Signals and Static Models

Determining the Dominant Features

Prediction of Tool Wear

Experimental Setup

Effects of Different Numbers of Retained Singular Values q and Dominant Features p

Comparison of Proposed Dominant Feature Identification (DFI) and Principal Feature Analysis (PFA)

Time Series Forecasting Using Acoustic Emission Signals and Dynamic Models

ARMAX Model Based on DFI

Experimental Setup

Comparison of Standard Non-Dynamic Prediction Models with Dynamic ARMAX Model

Comparison of Proposed ARMAX Model using ELS with DFI, MRM using RLS with DFI, and MRM using RLS with Principal Feature Analysis (PFA)

Effects of Different Numbers of Retained Singular Values and Features Selected

Comparison of Tool Wear Prediction Using AE Measurements and Force Measurements

DFI for Industrial Fault Detection and Isolation (FDI)

Augmented Dominant Feature Identification (ADFI)

Decentralized Dominant Feature Identification (DDFI)

Fault Classification with Neural Networks

Experimental Setup

Fault Detection Using 120 Features

Augmented Dominant Feature Identification (ADFI) and NN for Fault Detection

Decentralized Dominant Feature Identification (DDFI) and NN for Fault Detection


Probabilistic Small Signal Stability Assessment


Power System Modeling: Differential Equations

Synchronous Machines

Exciter and Automatic Voltage Regulator (AVR)

Speed Governor and Steam Turbine

Interaction Between A Synchronous Machine and its Control Systems

Power System Modeling: Algebraic Equations

Stator Equations

Network Admittance Matrix YN

Reduced Admittance Matrix YR

State Matrix and Critical Modes

Eigenvalue Sensitivity Matrix

Sensitivity Analysis of the New England Power System

Statistical Functions

Single Variate Normal PDF of αi

Multivariate Normal PDF

Probability Calculations

Small Signal Stability Region

Impact of Induction Motor Load

Composite Load Model for Sensitivity Analysis

Motor Load Parameter Sensitivity Analysis

Parametric Changes and Critical Modes Mobility

Effect of the Number of IMs on Overall Sensitivity (with 30% IM load)

Effect On Overall Sensitivity with Different Percentages of IM Load in the Composite Load



Discrete Event Command and Control


Discrete Event C2 Structure For Distributed Teams

Task Sequencing Matrix (TSM)

Resource Assignment Matrix (RAM)

Programming Multiple Missions

Conjunctive Rule-Based Discrete Event Controller (DEC)

DEC State Equation

DEC Output Equations

DEC as a Feedback Controller

Functionality of the DEC

Properness and Fairness of the DEC Rule Base

Disjunctive Rule-Based Discrete Event Controller (DEC)

DEC Simulation and Implementation

Simulation of Networked Team Example

Implementation of Networked Team Example on Actual WSN

Simulation of Multiple Military Missions Using FCS


Future Challenges

Energy-Efficient Manufacturing

Life Cycle Assessment (LCA)

System of Systems (SoS)



About the Authors

Chee Khiang Pang is an Assistant Professor in the Department of Electrical and Computer Engineering at National University of Singapore.

Frank L. Lewis is a Professional Engineer and Head of Advanced Controls and Sensors Group at the Automation and Robotics Research Institute, The University of Texas at Arlington.

Tong Heng Lee is Professor and cluster Head for the Department of Electrical and Computer Engineering at National University of Singapore.

Zhao Yang Dong is Associate Professor for the Department of Electrical Engineering at The Hong Kong Polytechnic University.

About the Series

Automation and Control Engineering

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Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Networking / General